Visual processing in the central bee brain

Visual scenes comprise enormous amounts of information from which nervous systems extract behaviorally relevant cues. In most model systems, little is known about the transformation of visual information as it occurs along visual pathways. We examined how visual information is transformed physiologically as it is communicated from the eye to higher-order brain centers using bumblebees, which are known for their visual capabilities. We recorded intracellularly in vivo from 30 neurons in the central bumblebee brain (the lateral protocerebrum) and compared these neurons to 132 neurons from more distal areas along the visual pathway, namely the medulla and the lobula. In these three brain regions (medulla, lobula, and central brain), we examined correlations between the neurons' branching patterns and their responses primarily to color, but also to motion stimuli. Visual neurons projecting to the anterior central brain were generally color sensitive, while neurons projecting to the posterior central brain were predominantly motion sensitive. The temporal response properties differed significantly between these areas, with an increase in spike time precision across trials and a decrease in average reliable spiking as visual information processing progressed from the periphery to the central brain. These data suggest that neurons along the visual pathway to the central brain not only are segregated with regard to the physical features of the stimuli (e.g., color and motion), but also differ in the way they encode stimuli, possibly to allow for efficient parallel processing to occur

Bayesian Time-Series Classifier for Decoding Simple Visual Stimuli from Intracranial Neural Activity

Understanding how external stimuli are encoded in distributed neural activity is of significant interest in clinical and basic neuroscience. To address this need, it is essential to develop analytical tools capable of handling limited data and the intrinsic stochasticity present in neural data. In this study, we propose a straightforward Bayesian time series classifier (BTsC) model that tackles these challenges whilst maintaining a high level of interpretability. We demonstrate the classification capabilities of this approach by utilizing neural data to decode colors in a visual task. The model exhibits consistent and reliable average performance of 75.55% on 4 patients' dataset, improving upon state-of-the-art machine learning techniques by about 3.0 percent. In addition to its high classification accuracy, the proposed BTsC model provides interpretable results, making the technique a valuable tool to study neural activity in various tasks and categories. The proposed solution can be applied to neural data recorded in various tasks, where there is a need for interpretable results and accurate classification accuracy

Insects modify their behaviour depending on the feedback sensor used when walking on a trackball in virtual-reality

When using virtual-reality paradigms to study animal behaviour, careful attention must be paid to how the animal's actions are detected. This is particularly relevant in closed-loop experiments where the animal interacts with a stimulus. Many different sensor types have been used to measure aspects of behaviour, and although some sensors may be more accurate than others, few studies have examined whether, and how, such differences affect an animal's behaviour in a closed-loop experiment. To investigate this issue, we conducted experiments with tethered honeybees walking on an airsupported trackball and fixating a visual object in closed-loop. Bees walked faster and along straighter paths when the motion of the trackball was measured in the classical fashion - using optical motion sensors repurposed from computer mice - than when measured more accurately using a computer vision algorithm called 'FicTrac'. When computer mouse sensors were used to measure bees' behaviour, the bees modified their behaviour and achieved improved control of the stimulus. This behavioural change appears to be a response to a systematic error in the computer mouse sensor that reduces the sensitivity of this sensor system under certain conditions. Although the large perceived inertia and mass of the trackball relative to the honeybee is a limitation of tethered walking paradigms, observing differences depending on the sensor system used to measure bee behaviour was not expected. This study suggests that bees are capable of fine-tuning their motor control to improve the outcome of the task they are performing. Further, our findings show that caution is required when designing virtual-reality experiments, as animals can potentially respond to the artificial scenario in unexpected and unintended ways

An Automated Paradigm for Drosophila Visual Psychophysics

Background: Mutations that cause learning and memory defects in Drosophila melanogaster have been found to also compromise visual responsiveness and attention. A better understanding of attention-like defects in such Drosophila mutants therefore requires a more detailed characterization of visual responsiveness across a range of visual parameters. Methodology/Principal Findings: We designed an automated behavioral paradigm for efficiently dissecting visual responsiveness in Drosophila. Populations of flies walk through multiplexed serial choice mazes while being exposed to moving visuals displayed on computer monitors, and infra-red fly counters at the end of each maze automatically score the responsiveness of a strain. To test our new design, we performed a detailed comparison between wild-type flies and a learning and memory mutant, dunce. We first confirmed that the learning mutant dunce displays increased responsiveness to a black/green moving grating compared to wild type in this new design. We then extended this result to explore responses to a wide range of psychophysical parameters for moving gratings (e.g., luminosity, contrast, spatial frequency, velocity) as well as to a different stimulus, moving dots. Finally, we combined these visuals (gratings versus dots) in competition to investigate how dunce and wild-type flies respond to more complex and conflicting motion effects. Conclusions/Significance: We found that dunce responds more strongly than wild type to high contrast and highly structured motion. This effect was found for simple gratings, dots, and combinations of both stimuli presented in competition

Center-For-Neurotechnology/MicrosEEG_Data_Analysis: v0.0.1

<p>Initiation of the shared code for processing neural data gathered using the micro-sEEG device. Additional code will be added.</p&gt

Higher order visual input to the mushroom bodies in the bee, Bombus impatiens

To produce appropriate behaviors based on biologically relevant associations, sensory pathways conveying different modalities are integrated by higher-order central brain structures, such as insect mushroom bodies. To address this function of sensory integration, we characterized the structure and response of optic lobe (OL) neurons projecting to the calyces of the mushroom bodies in bees. Bees are well known for their visual learning and memory capabilities and their brains possess major direct visual input from the optic lobes to the mushroom bodies. To functionally characterize these visual inputs to the mushroom bodies, we recorded intracellularly from neurons in bumblebees (Apidae: Bombus impatiens) and a single neuron in a honeybee (Apidae: Apis mellifera) while presenting color and motion stimuli. All of the mushroom body input neurons were color sensitive while a subset was motion sensitive. Additionally, most of the mushroom body input neurons would respond to the first, but not to subsequent, presentations of repeated stimuli. In general, the medulla or lobula neurons projecting to the calyx signaled specific chromatic, temporal, and motion features of the visual world to the mushroom bodies, which included sensory information required for the biologically relevant associations bees form during foraging tasks

Color processing in the medulla of the bumblebee (Apidae: Bombus impatiens)

The mechanisms of processing a visual scene involve segregating features (such as color) into separate information channels at different stages within the brain, processing these features, and then integrating this information at higher levels in the brain. To examine how this process takes place in the insect brain, we focused on the medulla, an area within the optic lobe through which all of the visual information from the retina must pass before it proceeds to central brain areas. We used histological and immunocytochemical techniques to examine the bumblebee medulla and found that the medulla is divided into eight layers. We then recorded and morphologically identified 27 neurons with processes in the medulla. During our recordings we presented color cues to determine whether response types correlated with locations of the neural branching patterns of the filled neurons among the medulla layers. Neurons in the outer medulla layers had less complex color responses compared to neurons in the inner medulla layers and there were differences in the temporal dynamics of the responses among the layers. Progressing from the outer to the inner medulla, neurons in the different layers appear to process increasingly complex aspects of the natural visual scene